Enhanced multimodal recommendation systems through reviews integration: Enhanced multimodal recommendation...: H. Fang et al.
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| Title: | Enhanced multimodal recommendation systems through reviews integration: Enhanced multimodal recommendation...: H. Fang et al. |
|---|---|
| Authors: | Fang, Hong1 (AUTHOR) fanghong@sspu.edu.cn, Liang, Jindong2 (AUTHOR) 20221513006@sspu.edu.cn, Sha, Leiyuxin2 (AUTHOR) 20221513026@sspu.edu.cn |
| Source: | Knowledge & Information Systems. Apr2025, Vol. 67 Issue 4, p3459-3486. 28p. |
| Subjects: | Learning modules, Information storage & retrieval systems, User experience, Systems theory, Noise, Recommender systems |
| Abstract: | Multimodal recommendation systems aim to capture diverse user preferences through data such as text and images, offering more personalized recommendation services. Accurately grasping user preferences can enhance the precision of recommendations and augment the user experience. Existing multimodal graph learning models enhance item representations by capturing the latent attribute relationships between items. However, multimodal features often suffer from modality missing issues, which hinder the model's ability to fully capture the connections between item attributes. Meanwhile, review data, as a form of textual information, not only provide descriptions of item attributes but also directly reflect user preferences. However, directly incorporating review data into item representations may introduce additional noise, potentially affecting the model's performance. Therefore, we propose the Personalized Multi-Preference Recommender (PMPR) model, which integrates reviews with multimodal data to extract multifaceted user preferences, enhancing the personalization of recommendations. Specifically, we designed a heterogeneous graph learning module based on reviews and a homogeneous graph learning module that combines the review features with multimodal features to capture users' diverse preferences. Considering the varying informational content of reviews, PMPR processes each review individually and utilizes user IDs to generate review attention vectors for aggregating the review features to reduce review noise. Finally, we integrate the Top-K method for recommendation. We compare common review processing methods with PMPR's approach to validate its effectiveness. In comparative analyses across five publicly available datasets, our enhanced model consistently demonstrated superior performance when benchmarked against seven widely recognized models. The results indicate a noteworthy improvement over the current state-of-the-art (SOTA) model, ranging from 2.76% to 22.52% in terms of average performance. [ABSTRACT FROM AUTHOR] |
| Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 184324763 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhanced multimodal recommendation systems through reviews integration: Enhanced multimodal recommendation...: H. Fang et al. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fang%2C+Hong%22">Fang, Hong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fanghong@sspu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liang%2C+Jindong%22">Liang, Jindong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> 20221513006@sspu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Sha%2C+Leiyuxin%22">Sha, Leiyuxin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> 20221513026@sspu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Knowledge+%26+Information+Systems%22">Knowledge & Information Systems</searchLink>. Apr2025, Vol. 67 Issue 4, p3459-3486. 28p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Learning+modules%22">Learning modules</searchLink><br /><searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink><br /><searchLink fieldCode="DE" term="%22User+experience%22">User experience</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+theory%22">Systems theory</searchLink><br /><searchLink fieldCode="DE" term="%22Noise%22">Noise</searchLink><br /><searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Multimodal recommendation systems aim to capture diverse user preferences through data such as text and images, offering more personalized recommendation services. Accurately grasping user preferences can enhance the precision of recommendations and augment the user experience. Existing multimodal graph learning models enhance item representations by capturing the latent attribute relationships between items. However, multimodal features often suffer from modality missing issues, which hinder the model's ability to fully capture the connections between item attributes. Meanwhile, review data, as a form of textual information, not only provide descriptions of item attributes but also directly reflect user preferences. However, directly incorporating review data into item representations may introduce additional noise, potentially affecting the model's performance. Therefore, we propose the Personalized Multi-Preference Recommender (PMPR) model, which integrates reviews with multimodal data to extract multifaceted user preferences, enhancing the personalization of recommendations. Specifically, we designed a heterogeneous graph learning module based on reviews and a homogeneous graph learning module that combines the review features with multimodal features to capture users' diverse preferences. Considering the varying informational content of reviews, PMPR processes each review individually and utilizes user IDs to generate review attention vectors for aggregating the review features to reduce review noise. Finally, we integrate the Top-K method for recommendation. We compare common review processing methods with PMPR's approach to validate its effectiveness. In comparative analyses across five publicly available datasets, our enhanced model consistently demonstrated superior performance when benchmarked against seven widely recognized models. The results indicate a noteworthy improvement over the current state-of-the-art (SOTA) model, ranging from 2.76% to 22.52% in terms of average performance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10115-024-02309-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 3459 Subjects: – SubjectFull: Learning modules Type: general – SubjectFull: Information storage & retrieval systems Type: general – SubjectFull: User experience Type: general – SubjectFull: Systems theory Type: general – SubjectFull: Noise Type: general – SubjectFull: Recommender systems Type: general Titles: – TitleFull: Enhanced multimodal recommendation systems through reviews integration: Enhanced multimodal recommendation...: H. Fang et al. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fang, Hong – PersonEntity: Name: NameFull: Liang, Jindong – PersonEntity: Name: NameFull: Sha, Leiyuxin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02191377 Numbering: – Type: volume Value: 67 – Type: issue Value: 4 Titles: – TitleFull: Knowledge & Information Systems Type: main |
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